Handling hypercolumn deep features in machine learning for rice leaf disease classification
Yazarlar (1)
Prof. Dr. Kemal AKYOL Kastamonu Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Multimedia Tools and Applications (Q2) | ||
| Dergi ISSN | 1380-7501 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 04-2023 |
| Kabul Tarihi | 10-12-2022 | Yayınlanma Tarihi | 17-12-2022 |
| Cilt / Sayı / Sayfa | 82 / 13 / 19503–19520 | DOI | 10.1007/s11042-022-14318-5 |
| Makale Linki | https://doi.org/10.1007/s11042-022-14318-5 | ||
| Özet |
| Rice leaf disease, which is a plant disease, causes a decrease in rice production and more importantly, environmental pollution. 10–15% of the losses in rice production are due to rice plant diseases. Automatic recognition of rice leaf disease by computer-assisted expert systems is a promising solution to overcome this problem and to bear the shortage of field experts in this field. Many studies have been conducted using features extracted from deep learning architectures, so far. This study includes keypoint detection on the image, hypercolumn deep feature extraction from CNN layers, and classification stages. The hypercolumn is a vector that contains the activations of all CNN layers for a pixel. Keypoints are prominent points in the images that define what stands out in the image. The first step of the model proposed in this study includes the detection of keypoints on the image and then the extraction of … |
| Anahtar Kelimeler |
| Deep learning | Hypercolumn deep features | Important keypoint detection | Machine learning | Rice leaf disease |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 10 |
| Google Scholar | 13 |